ABSTRACT: DATA MANAGEMENT AND ANALYSIS CORE The Data Management and Analysis Core (DMAC) provides critical support for UNC-SRP researchers to manage and analyze data related to the theme, ?Identifying novel methods to reduce iAs exposure and elucidating mechanisms underlying iAs-induced metabolic dysfunction with a vision for disease prevention.? The goal of the DMAC is to support the data management, integration, and analysis needs of the UNC-SRP researchers to reveal multi-factorial determinants of iAs-induced metabolic dysfunction/diabetes. The data management component of DMAC is housed in the Renaissance Computing Institute (RENCI). The analytical support of the DMAC is made possible through the UNC-Department of Statistics. Each of the projects in the SRP will work closely with DMAC for bioinformatic, statistical, and data science needs. By making data Findable, Accessible, Interoperable and Reusable (FAIR), the DMAC will maximize the impact and optimize the path to identifying high impact insights. The UNC-SRP scientists in Biomedical Projects 1-3 will generate a broad suite of data types that includes iAs-associated microRNAs, transcription factors, and bacterial presence in the gut microbiome. Environmental Projects 4-5 will generate data relevant to iAs levels in North Carolina (NC) and iAs reduction via filtration. The DMAC will provide state-of-the-art data management, stewardship, and analysis for these diverse types of data. Specifically, the DMAC will: first, develop UNC-SRP-wide comprehensive Data Management Plan to include high quality data generation and systems that foster sharing and interoperability; second, facilitate UNC-SRP Project-specific research activities by implementing state-of-the-art bioinformatic and biostatistical methods; and third, integrate UNC-SRP data across Projects 1-5 to understand risks of iAs exposure and mechanisms underlying iAs-associated metabolic dysfunction/diabetes. Together, the DMAC will provide the UNC-SRP with essential expertise in data management, bioinformatics, statistics, and data integration and critically contribute to the team fulfilling its mission- to develop new solutions for iAs reduction and disease prevention through mechanistic and translational research.